academic publishing

This study addresses the ambiguity and lack of quantifiable assessment in author contribution allocation within scientific collaboration. Leveraging author contribution statements from over 400,000 papers, we construct the first large-scale computational framework for mapping free-text contributions to the 14 standardized CRediT roles. Our analysis reveals a significant gradient association between author position and task type: early-positioned authors predominantly perform experimental and analytical tasks, whereas last-positioned authors concentrate on leadership and management responsibilities. Within small teams, individual task loads vary by over threefold, with disparities scaling linearly with team size. We standardize 5.6 million author–task assignments across 1.58 million author mentions. This constitutes the first empirical demonstration that contemporary scientific collaboration exhibits a “position-driven” center–periphery division of labor and a hierarchical role stratification.

研究通过采访技术作家探讨了软件文档审查过程及其挑战，识别了五个审查阶段，并揭示了组织和技术上的难题。

This study investigates key determinants of paper acceptance in open peer review, moving beyond static manuscript features to model the dynamic review process. Leveraging complete interaction data from over 28,000 submissions to ICLR 2017–2025, we integrate textual features, temporal reviewer–author behaviors, inter-reviewer disagreement metrics, and meta-review trajectories. Our analysis reveals—first time systematically—that response timeliness and interaction quality during the rebuttal phase exert a stronger influence on final decisions than initial review scores. Key findings indicate that clear writing, balanced figure usage, early submission, constructive and proactive author responses, and effective reconciliation of reviewer disagreements significantly increase acceptance probability. These results provide empirically validated, data-driven insights to enhance transparency, fairness, and efficiency in open peer review systems.

To address the challenge of ensuring domain expertise alignment in reviewer assignment amid the rapid proliferation of papers in emerging research areas, this paper proposes an automated reviewer recommendation method that jointly leverages citation networks and weighted academic impact metrics. The method innovatively integrates co-occurrence statistics of cited authors, dynamically weighted ranking based on multidimensional scholarly indicators (h-index, i10-index, and citation count), structured information extraction from author homepages, and collaborative relationship filtering. It encompasses a pipeline comprising web crawling, keyword extraction, academic metric parsing, and collaborative deduplication. Evaluated on a real-world paper dataset, the approach significantly improves recommendation relevance, precision, and domain specificity. It enables end-to-end automated reviewer selection and provides a scalable, high-accuracy solution for scholarly publishing peer-review workflows.

In conference peer review, misaligned incentives among authors, conferences, and reviewers stem from inherent noise in paper quality assessment. Method: We formulate a Stackelberg game between authors and conferences, introducing the novel concept of “resubmission gap,” and analyze the dynamic trade-offs among acceptance thresholds, author resubmission behavior, and reviewer load via agent-based simulation, noise-aware modeling, and parameter estimation from historical data. Contributions/Results: (1) Raising the acceptance threshold reduces reviewer burden while preserving accepted paper quality; (2) Author self-selection induces a counterintuitive effect: stricter review increases the acceptance rate of high-quality papers; (3) A small number of high-quality reviews combined with a high threshold outperforms a large volume of low-quality reviews, and reusing prior reviews significantly alleviates load without compromising quality. We quantify how key parameters affect system performance, providing both theoretical foundations and empirical support for conference policy design.

This work proposes a novel journal management system that integrates closed-loop control with a multi-agent architecture to address persistent challenges in academic publishing, including submission overload, reviewer fatigue, inconsistent evaluations, opaque governance, and vulnerability to manipulation. The system orchestrates dynamic collaboration among authors, human and AI reviewers, and rotating editors through adaptive policies and diverse feedback mechanisms, enabling a decentralized, self-regulating, and auditable publication process. It innovatively incorporates non-permanent role assignments, human-led decision-making, data confidentiality, and dynamic governance—features that transcend the limitations of traditional centralized models. Simulation experiments demonstrate that the approach effectively mitigates manuscript backlogs, balances reviewing workloads, suppresses collusive behavior, and exhibits robust, interpretable system responsiveness.

Traditional peer review is characterized by opacity, loose structure, and publisher dominance, limiting transparency and broad participation. This study systematically defines and empirically investigates informal peer review—a phenomenon integrating three variants of open peer review—for the first time. Employing a cross-platform digital ethnography approach, the research combines participant observation with open- and axial-coding qualitative analysis to trace reviewing practices and metacommentary across 15 online communities. Findings reveal that informal reviewers are highly diverse in identity, self-organize within rudimentary digital spaces, and deploy deep, unconventional strategies. Despite encountering resistance from authors and publishers, these practices demonstrate emergent potential as an evidence infrastructure. Building on these insights, the study proposes a scalable governance framework and pathways for tool optimization.

This study addresses the challenge of quantifying the dynamic relationship between peer reviewers’ critiques, authors’ revisions, and subsequent paper impact at scale. Leveraging a fixed-prompt large language model pipeline, the authors structurally parse open peer review correspondence from *Nature Communications* (2017–2024) to construct a reviewer–author interaction dataset. The analysis reveals that rigorous, high-quality review comments—and the corresponding substantive author revisions—significantly and positively predict a paper’s future citation impact, challenging the conventional assumption that high-quality manuscripts should sail smoothly through peer review. Furthermore, first-round reviews predominantly focus on core claims, and disciplinary differences in reviewing styles manifest primarily in content rather than in comment length.

为减轻元评审员负担，本文通过构建Metag数据集来识别论文在审稿-反驳过程中的修改，使用方法包括获取文稿版本、计算差异并由人工标注。

This study systematically investigates the application of artificial intelligence—particularly large language models—across the entire academic peer review pipeline, encompassing key stages such as review generation, author rebuttal, meta-reviewing, and manuscript revision. The work introduces the first end-to-end technical framework that integrates fine-tuning, agent-based systems, reinforcement learning, and multidimensional automated evaluation methods, comprehensively mapping existing datasets, modeling paradigms, and assessment strategies. Beyond offering practical guidelines for building end-to-end peer review assistance systems, the research also critically examines associated ethical challenges and outlines promising future directions, thereby providing both theoretical grounding and actionable insights for advancing AI-supported scholarly peer review.